{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "from keras.layers import Conv1D, Input, Activation\n",
    "from keras.models import Model\n",
    "import scipy.signal as sp\n",
    "import numpy as np\n",
    "import tables\n",
    "from scipy.io import wavfile\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "sns.set()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(61, 1, 1)\n"
     ]
    }
   ],
   "source": [
    "coeff_path='/media/taufiq/Data1/heart_sound/heartnetTransfer/filterbankcoeff60.mat'\n",
    "signal_path='/media/taufiq/Data1/Heart_Sound/Physionet/training/training-a/a0001.wav'\n",
    "coeff=tables.open_file(coeff_path)\n",
    "b1=coeff.root.b2[:]\n",
    "b1=np.hstack(b1)\n",
    "b1=np.reshape(b1,[b1.shape[0],1,1])\n",
    "print b1.shape\n",
    "fs,data=wavfile.read(signal_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "filtered=sp.filtfilt(b1[:,0,0],[1],data[:2500])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAY8AAAEACAYAAABLfPrqAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nk4eZmQ3t/5qjcl6tCbKeAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f921a807e50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(data[:2500],label='Raw data')\n",
    "plt.plot(filtered,label='After FIR1(zero phase)')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "def FIRnet_w_act(input_size):\n",
    "    input1=Input(shape=(input_size,1))\n",
    "    x = Conv1D(1,61,use_bias=False,weights=[b1],padding='same') (input1)\n",
    "    x = Activation(activation='relu') (x)\n",
    "    model =Model(input1,x)\n",
    "    model.compile(optimizer='rmsprop',\n",
    "                  loss='binary_crossentropy',\n",
    "                  metrics=['accuracy'])    \n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def FIRnet(input_size):\n",
    "    input1=Input(shape=(input_size,1))\n",
    "    x = Conv1D(1,61,use_bias=False, weights=[b1])(input1)\n",
    "    model =Model(input1,x)\n",
    "    model.compile(optimizer='rmsprop',\n",
    "                  loss='binary_crossentropy',\n",
    "                  metrics=['accuracy'])    \n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = FIRnet(2500)\n",
    "t = np.reshape(data[:2500],[1,2500,1])\n",
    "new_data_ = model.predict(t)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = FIRnet_w_act(2500)\n",
    "t = np.reshape(data[:2500],[1,2500,1])\n",
    "new_data = model.predict(t)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYMAAAD8CAYAAACVZ8iyAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMS4wLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvpW3flQAAIABJREFUeJzt3Xd4VFX6wPHvO5NG7yBVAgLSWyxIsaLYxQq7ioqK6NpWZdXVdbH9dlUsa1lcXBGwgJWi4ipFBaSDoQoSIEAiPRASQtrM+f1x7yST5GaSyQyESd7P88yTybnt3Jk7972n3HPFGINSSqnqzVXZGVBKKVX5NBgopZTSYKCUUkqDgVJKKTQYKKWUQoOBUkopNBgopZRCg4FSSik0GCillAKiKjsD5dW4cWPTtm3bys6GUkpFjFWrVh0wxjQpz7wREwzatm3LypUrKzsbSikVMURkR3nn1WoipZRSGgyUUkppMFBKKUUEtRk4ycvLIyUlhezs7MrOilInTFxcHK1atSI6Orqys6KqkIgOBikpKdSpU4e2bdsiIpWdHaWOO2MMBw8eJCUlhfj4+MrOjqpCIrqaKDs7m0aNGmkgUNWGiNCoUSMtDauwCyoYiMhEEdknIuv90hqKyBwR2WL/bWCni4i8ISJJIrJWRPr4LXOrPf8WEbk1lB3QQKCqGz3m1fEQbMlgEjCkWNrjwDxjTAdgnv0/wKVAB/s1ChgPVvAA/g6cBZwJ/N0XQJRSFTMzMZUj2XmVnQ0VwYIKBsaYBUBaseSrgcn2+8nANX7pU4xlKVBfRJoDlwBzjDFpxphDwBxKBpiIMmPGDESETZs2FUkfM2YMXbt2ZcyYMcyYMYONGzdWUg7Db+XKlTzwwAMA/PjjjyxevLhg2m233cbnn39eWVkLm9L24+mnn2bu3LlBr+/333/n+uuvd5x23nnnVfimyq37M3lwWiJ/npZYoeWVgvA0IDczxuy23+8BmtnvWwK7/OZLsdNKS49YU6dOZcCAAUydOpVnnnmmIH3ChAmkpaXhdru57bbbuOKKK+jSpUu515ufn09U1MnZxp+QkEBCQgJgBYPatWtzzjnnnJBtezwe3G73CdmWk2effbZCy7Vo0eK4BEmv1wCQfPBo2Netqo+wNiAbYwxgwrU+ERklIitFZOX+/fvDtdqwyszMZNGiRbz33ntMmzatIP2qq64iMzOTvn378swzzzBr1izGjBlDr1692Lp1K1u3bmXIkCH07duXgQMHFpQqbrvtNkaPHs1ZZ53FX/7ylyLb8ng8PProo3Tr1o0ePXrw5ptvAjBv3jx69+5N9+7dGTlyJDk5OYA1hMff//53+vTpQ/fu3dm0aRNer5e2bdty+PDhgvV26NCBvXv3FtlW9+7dOXz4MMYYGjVqxJQpUwAYMWIEc+bM4ccff+SKK64gOTmZd955h9dee41evXqxcOFCABYsWMA555xDu3btHE+A77zzDr169aJXr17Ex8dz/vnnA/D999/Tr18/+vTpww033EBmZmbBvjz22GP06dOHzz77jMTERM4++2x69OjB0KFDOXToUIlt+D7LhIQEOnbsyNdffw1AcnIyAwcOpE+fPvTp06egVGOM4b777qNTp05cdNFF7Nu3z/E79y8xOH3GAD/99FPB/vXu3ZuMjAySk5Pp1q0bAMeOHWPYsGF07tyZoUOHcuzYsYL1l/YZlCbabf2Mcz3egPMpFUg4Ljv3ikhzY8xuuxrI9wtKBVr7zdfKTksFziuW/qPTio0xE4AJAAkJCQGDzDNfbWDj70cqkv9SdWlRl79f2TXgPDNnzmTIkCF07NiRRo0asWrVKvr27cusWbOoXbs2iYlW0X379u1cccUVBdUEF154Ie+88w4dOnRg2bJl3HvvvcyfPx+wuswuXry4xNXvhAkTSE5OJjExkaioKNLS0sjOzua2225j3rx5dOzYkREjRjB+/HgeeughABo3bszq1av597//zbhx4/jvf//L1VdfzfTp07n99ttZtmwZp556Ks2aNSuyrf79+/Pzzz9z6qmn0q5dOxYuXMiIESNYsmQJ48ePZ8WKFYB1Mhw9ejS1a9fm0UcfBeC9995j9+7dLFq0iE2bNnHVVVeVqB4ZPXo0o0ePJi8vjwsuuICHH36YAwcO8PzzzzN37lxq1arFiy++yKuvvsrTTz8NQKNGjVi9ejVAQTA899xzefrpp3nmmWd4/fXXS3w/ycnJLF++nK1bt3L++eeTlJRE06ZNmTNnDnFxcWzZsoXhw4ezcuVKpk+fzubNm9m4cSN79+6lS5cujBw5soyjxPkzHjduHG+//Tb9+/cnMzOTuLi4IsuMHz+emjVr8uuvv7J27Vr69LH6V5T1GTjx/TA8nrBdh6lqKBwlg1mAr0fQrcBMv/QRdq+is4F0uzrpO+BiEWlgNxxfbKdFpKlTpzJs2DAAhg0bxtSpU8tcJjMzk8WLF3PDDTfQq1cv7r77bnbv3l0w/YYbbnCsBpk7dy533313QdVRw4YN2bx5M/Hx8XTs2BGAW2+9lQULFhQsc+211wLQt29fkpOTAbjpppv45JNPAJg2bRo33XRTiW0NHDiQBQsWsGDBAu655x7WrVtHamoqDRo0oFatWmXu4zXXXIPL5aJLly4lSh3+HnzwQS644AKuvPJKli5dysaNG+nfvz+9evVi8uTJ7NhROM6WL5/p6ekcPnyYc88913Gf/d144424XC46dOhAu3bt2LRpE3l5edx11110796dG264oaAtZ8GCBQwfPhy3202LFi244IILytxPcP6M+/fvz8MPP8wbb7zB4cOHS1T3LViwgJtvvhmwAluPHj0AyvwMnHjsaiKXS3sZqYoLqmQgIlOxruobi0gKVq+gfwKfisgdwA7gRnv22cBlQBKQBdwOYIxJE5HngBX2fM8aY4o3SgetrCv44yEtLY358+ezbt06RASPx4OI8PLLLwfs/uf1eqlfv35BqaG48pxsyys2NhYAt9tNfn4+AP369SMpKYn9+/czY8YMnnrqqRLLDRo0iLfffpudO3fywgsvMH36dD7//HMGDhwY1HbBqn5xMmnSJHbs2MFbb71VMN/gwYNLDagV+VyKfw8iwmuvvUazZs1Ys2YNXq+3xFV7sJw+48cff5zLL7+c2bNn079/f7777rtybaesz8CJLxhoj1MVimB7Ew03xjQ3xkQbY1oZY94zxhw0xlxojOlgjLnId2K3exH9yRjT3hjT3Riz0m89E40xp9mv98O9UyfK559/zi233MKOHTtITk5m165dxMfHF9Sb+6tTpw4ZGRkA1K1bl/j4eD777DPAOgGsWbOmzO0NHjyY//znPwUnnLS0NDp16kRycjJJSUkAfPDBBwVXzKUREYYOHcrDDz9M586dadSoUYl5WrduzYEDB9iyZQvt2rVjwIABjBs3jkGDBgXct/JatWoV48aN48MPP8Tlsg7Ds88+m59//rlgX44ePcpvv/1WYtl69erRoEGDgs850D5/9tlneL1etm7dyrZt2+jUqRPp6ek0b94cl8vFBx98gMfjAawA+Mknn+DxeNi9ezc//PBDUPvkb+vWrXTv3p3HHnuMM844o0RPs0GDBvHxxx8DsH79etauXRvUZ+CvoGSg0UCFIKLvQK5sU6dOZejQoUXSrrvuOserumHDhvHyyy/Tu3dvtm7dykcffcR7771Hz5496dq1KzNnziyxTHF33nknbdq0oUePHvTs2ZOPP/6YuLg43n//fW644Qa6d++Oy+Vi9OjRZa7rpptu4sMPP3SsIvI566yzCqqfBg4cSGpqKgMGDCgx35VXXsn06dOLNCCX5a233iItLY3zzz+fXr16ceedd9KkSRMmTZrE8OHD6dGjB/369StxEvWZPHkyY8aMoUePHiQmJpZap96mTRvOPPNMLr30Ut555x3i4uK49957mTx5Mj179mTTpk0FJY6hQ4fSoUMHunTpwogRI+jXr1+59sXJ66+/XtDQHx0dzaWXXlpk+j333ENmZiadO3fm6aefpm/fvgBBfQY+XqPBQIVOSivCn2wSEhJM8X7Yv/76K507d66kHKmTna87b2l9+yOZ/7GfuOsw17z9M+2a1GL+I+dVbsbUSUVEVhljEsozr5YMlIpwWk2kwuHkvKNJqTCYNGlSZWfhBImM0r06uWnJQKkIFyE1veokp8FAqQjn1WCgwkCDgVIRLlI6gaiTmwYDpSKcLxRoUFCh0GAQBidqCOsff/yRevXqFQyAdtFFFwEwduxYxo0bB1jdKePj4+nVqxc9e/Zk3rx5IW1Tnfz8Y8C2/Zn8srPkoH1KlUWDQRj4D2Htb8KECaxdu5aXX365QsHAd6exv4EDB5KYmEhiYmKpY+q//PLLJCYm8vrrr5frBjQV2fxLBBe88hND/704wNxKOdNgEKITOYR1sPr160dqampI61AnP60cUuFQde4z+PZx2LMuvOs8pTtc+s+As5zIIawBFi5cSK9evQBrdNMnn3yy1Lz973//45prril1uqoatKlAhUPVCQaVZOrUqTz44INA4RDWvnFmSuM/hLWP74E0UPoQ1mBVE/ke0lKaMWPG8Ne//pWUlBSWLFlS3l1REcrYZQONCSoUVScYlHEFfzycrENYv/zyy1x//fW8+eabjBw5klWrVoW0PnVyc7rPwBgT8BhUqjhtMwjBiR7COlj33XcfXq+X776L2GcHqXJw6lLq0TvRVJA0GITgRA9hHSwR4amnnuKll14K+7rVycPptO/RhgQVJB3CWqkI5H/s/7BpH7dPWkG7xrXYduAoABufvYSaMVWnFlhVjA5hrVQ1YhzKBlpNpIKlwUCpCOf1lkzzBQNjDH/5fI3elazKFHIwEJFOIpLo9zoiIg+JyFgRSfVLv8xvmSdEJElENovIJaFsP1KquZQKl+LHvGObgR0MjmTn8+nKFG55b/kJyJmKZCFXKhpjNgO9AETEDaQC04HbgdeMMeP85xeRLsAwoCvQApgrIh2NMZ5gtx0XF8fBgwdp1KiRdqNT1YIxhoMHDxIXF1ckrbji1UTZeUH/vFQ1E+4WpguBrcaYHQFOzlcD04wxOcB2EUkCzgSCvjuqVatWpKSksH///gpnWKlIExcXR6tWrQr+N0Bf2YzHtMf3k/b1Jsr3WHVI+dqGoMoQ7mAwDPDvV3mfiIwAVgKPGGMOAS2BpX7zpNhpJYjIKGAUQJs2bUpMj46OJj4+Pjw5VypCGa+XL2KfIflYa87jRaCwZKANyaq8wtaALCIxwFXAZ3bSeKA9VhXSbuCVYNdpjJlgjEkwxiQ0adIkXFlVqmrx5gHQ1rurIMkXBHwlApfWoqoyhLM30aXAamPMXgBjzF5jjMcY4wXexaoKAqtNobXfcq3sNKVUBbjys0ukFS8ZuLRNTZUhnMFgOH5VRCLS3G/aUGC9/X4WMExEYkUkHugAaFcHpSrIFww8fj9nXxDIs9sMNBiosoSlzUBEagGDgbv9kl8SkV5Y7VvJvmnGmA0i8imwEcgH/lSRnkRKKYtV+Abx62Tqa0D2+noaaSxQZQhLMDDGHAUaFUu7JcD8LwAvhGPbSlV3vmDg8gsG+R5fMKiULKkIpHcgKxXpTMlbkH0lgoJbEDQoqDJoMFAq0jncdOYrETiNW6SUEw0GSkW6AHcg+8Yt0qCgyqLBQKkI59T/wjdEhQYBVV4aDJSKcIHGJvJN0vEcVVk0GCgV6QK1GRS0HSgVmAYDpSKccehNVLyaSId6V2XRYKBUpHN4uk3hTWfW/xoKVFk0GCgV8QJVE2kYUOWjwUCpSOd005m3WMlAY4IqgwYDpSKc09W/V289VkHSYKBUhHNqQC7oWurxMD/mYS5zLS0xj1L+NBgoFekCdC2VvKO0c+1hXPR/TnCmVKTRYKBUhHO6y7iga6nXuju5puSc0DypyKPBQKkIJwG6luLJO8G5UZEqLM8zUEpVpsKSQW/ZQi5ReE1vK8F+PrLXiF75qYA0GCgV4XxVQQDTY/8OwAzvUADEmw+AFw0GKjA9PpSKcE6dR905h2FsPRpvnQ4UfT6yUk7CdoSISLKIrBORRBFZaac1FJE5IrLF/tvAThcReUNEkkRkrYj0CVc+lKp2HNoM4jJ3AdBq4zt2ij4EWQUW7suF840xvYwxCfb/jwPzjDEdgHn2/wCXAh3s1yhgfJjzoVQ14tC1VKwaYJc3r9R5lPJ3vMuOVwOT7feTgWv80qcYy1Kgvog0P855UapqcrjpLB83AOIwTSkn4QwGBvheRFaJyCg7rZkxZrf9fg/QzH7fEtjlt2yKnaaUCpbTw22k6E9btGSgyhDO3kQDjDGpItIUmCMim/wnGmOMiAR1RNpBZRRAmzZtwpdTpaoSp+cZeIv+1LTFQJUlbCUDY0yq/XcfMB04E9jrq/6x/+6zZ08FWvst3spOK77OCcaYBGNMQpMmTcKVVaWqFKeB6kyxRmUtGaiyhCUYiEgtEanjew9cDKwHZgG32rPdCsy0388CRti9is4G0v2qk5RSwXB80pmnyP8aDFRZwlVN1AyYLiK+dX5sjPmfiKwAPhWRO4AdwI32/LOBy4AkIAu4PUz5UKracRybyONxmFOp0oUlGBhjtgE9HdIPAhc6pBvgT+HYtlLVnsN9Btm5Rcck0jYDVRa9LVGpCOdUBZRy4HCR/13B9d1Q1ZAGA6UinUObQbTR0UpVcDQYKBXhnHoTuTUYqCBpMFAq0jkEA/HkVkJGVCTTYKBUxHPoWqrBQAVJg4FSkc6hN5E+4UwFS4OBUhHPoZrIq8FABUeDgVKRzqE3ke8JZ0qVlwYDpSKdwy0EGgxUsDQYKBXxSg49odVEKlgaDJSKcA49S51LBm/0gS/uOv4ZUhFJg4FSkc6hzcDxprO0rbDu0xOQIRWJNBgoFfEcehMZbTNQwdFgoFSEE4f7DFzagKyCpMFAhSzlUBZtH/+GX3YequysVFM6NpEKnQYDFbKFWw4AMHX5zkrOSTXl0Gbg0moiFSQNBipsnHq1qBPAcdRSDQYqOBoMVNi4RHh1zm+c9X9zKzsr1YrTYy81GKhghesZyKoay823qilioly8MW8LYI2xbz8TWx1n4ti1NF+fdamCEnLJQERai8gPIrJRRDaIyIN2+lgRSRWRRPt1md8yT4hIkohsFpFLQs2Dqlw5+dYdsDFRhYdTrsdhJE0VfnnZjsNRaJuBClY4Sgb5wCPGmNUiUgdYJSJz7GmvGWPG+c8sIl2AYUBXoAUwV0Q6GmNK3lOvIoLvvB/lKrwUzcn3EhvlrqQcVRNHfodXO3NGjU4lJkU5DFGhVCAhlwyMMbuNMavt9xnAr0DLAItcDUwzxuQYY7YDScCZoeZDVR5fbZD/BWpOnpYMjrv0VABaHNtcYlIUWjJQwQlrA7KItAV6A8vspPtEZK2ITBSRBnZaS2CX32IpBA4e6iTn8VphwP9ZvL6qI3UcBWiTidaSgQpS2IKBiNQGvgAeMsYcAcYD7YFewG7glQqsc5SIrBSRlfv37w9XVlWYFQaDwrScfC0ZHHcODcc+0VoyUEEKSzAQkWisQPCRMeZLAGPMXmOMxxjjBd6lsCooFWjtt3grO60EY8wEY0yCMSahSZMm4ciqOg58wcDrHwy0muj4C/BoS20zUMEKR28iAd4DfjXGvOqX3txvtqHAevv9LGCYiMSKSDzQAVgeaj5U5fEFA4/fGDlaTXQCBHhmQbTo56+CE47eRP2BW4B1IpJop/0VGC4ivbDaFZOBuwGMMRtE5FNgI1ZPpD9pT6LI5jGG5hwkz9umIC3Po7cjH3cBBqMLVE3k8RrcLr0JQRUVcjAwxizC+faW2QGWeQF4IdRtq5ND/aPbWRJ3P1/v/RMf0x+AfL3P4PjzlH7CD1RNlOfx4nZpt19VlA5HoUJWO3sPAB0ylhak5Xm1ZHDcBaomChAMvDqIlHKgwUCFLN8+jPyHRcj3eMnJ93A4K7fg/1ztYRReARqQA1YTpe2A9JTjkSMVwTQYqJD5Go5d3sKr0TyP4aFpifR6dg5er+G6d5bQbex3lZXFqslb+tV/oJvO6rzTB17ryr9/TOLL1RoUlEUHqlMhk4IrVP9g4OXb9Vb1UVaehzW7DldCzqq4EHsTvfa/DXgRru3TKpy5UhFKg4EKmXitqiAxXrrLNqLJJ9/bs2B6Vo7eAHU8ePPzSi3al+emsy1xI1jjbQdcFdZ8qcik1UQqZGJXV7iMh49i/o8vY8cW6Vp6NLfwKtVo42XYbP699MeMuilf+0xP17ZwZUdFOA0GKmTiKSwZ1JUsAPL9gkFWbuFVqg5TET4S4DnHOhyFCpYGAxUyscfOF/x7ExWWBrL8SgYaDMIn0DMLdKA6FSwNBipkLrvNwJjCew+9edkF7/2DgXYvDaOAN52Vv2TwwMermbZ8ZzhypCKYBgMVMrGHRfB/Fq8rN6PgfXaeBzC48eiYRWFkAgxHERPE2ESPbb6e3TPHhiFHKpJpMFBsP3CUT1fsKnvGUrjsLo5F2obzjhW8zc7zMDH6ZabGPK/VRGHkzcsNy3paykH+HP1FWNalIpcGgwrYlZbFrDW/l0hfvfMQt7y3zL4Sjhw3/WcJf/libfD5Tk+FKddQL/8AUHQIa/KyCp69kpXr4QJ3Ime6Nle5oa0378ng23W7K2XbngB3ICsVLA0GFfDoZ2t4YOovHMjMKZL+wje/snDLAdanpldSzipmX4a1HwePBnmluehV2PYD/TO/ByjSmdGVn00LDtJHfiMju/CklWcPYDczMZV1KZX3OeV7vAVDb4fiktcXcM9Hq/F6DYeO5pKT72HzngyOZId+ojbGBBzwz5uvwUCFjwaDClienIYLL2nFTp6+UYFzI2zEzjhyaC+pHMsNsjuiWCNfun29WvzrifKyeC/6Jb6MHUvm0cIqo7x8D7n5Xh6clsiVby0qSC+rYflvM9Yz/ZcUft19hO837Akqm9sPHOXhTxLJ9Lv5rc9zcxj+7tIASwVmjGH6L4VDORw+lkfv5+bw7Fcb+f6t+3h94gcVXveR7Dwyc/J5avpaOj/5Van3Zni1ZBBxPl62kyGvL8B7Eg7kqHcgV8DtUd/zqGsqv2b8As3qFKTHkEtX2U5WTkIl5i5470e/TD/3RhKPXsUPm49xXscmSIDn6/oYrwcBXHaZwO3X792Vn83pLqsdwpu5ryC91c9PcCwuDrikIG3q8p088eU61jx9MfVqRpfYTnaehw+W7uDDZTvo0zCPg2kHuPCFO8o9Jv+EBdv48pdUBndpxqXdrWcuHcnOZ/n2tCLzZebkM3bWBh68sAOtG9YsdX13f7CSOnHRrFq9gvbi5RLXSnYc6AfAt8vWszpuBul7vwdGlit/xd37wpvUi4XBOXP4c+x6di7NJ3rtVKKHf8j8zfu4MaE1IoKpYsEgKzefg5m5AT/7SPfX6esA2HMkmxb1a1RyborSYFABD7o+o6bkkHswGU5rWZD+h4xJXB47ne/TEoBmlZa/YPVzbwTgq4WrmP/rbjKGXcZVPVsA8PAniTSpE8sTl3UusVzS3nQ6ADWMdeUfZ3IKnmzh8mQVzOc+urfg/SlJn9jvCoPBOz9tBWBnWhbda9YrsZ20o7l8GvMMq70duS5zAU1i0zmYdQuNaseWa/+OZqRzmWspWTk9APB6DQNc69hvim5r/qZ9fL4qhVoxbp65upvjunLy8hm95W5STSPGxS4rSJ/z+yjudn/FZmM90bWeZDkuXx5TXM/iyjdgP3Kg8XdWUHlh8vs03ruQbY2fo/3yv1P7WFqAtQQvKzefmjFRfLB0B3+bsZ5Nzw0hLrr8zz3wle5iogJXOGTl5hPjdhHlLjrfnZNXsnjrQbb/47JyXYxEJqtEkHY0N2AwyPd4cYngOoEPIdJgEIRvvvqcI+v/xzViVTfkpdtXvNlHYPGbnJG9GACT7vhI5xMmO8/j+CM2xuA1lHpF3SdlMn+L/Y7PtzeAntcB8OUvqYDhTxecxs3/Xcbjl57OOe0bA7DjQAYd/JavQWF1kDe38D6DGkdL76lkjKG+OUx713oOHj0DsBpl69aIIu1oLsZArMtwpmszZ7o2Fyx36EhGmcEgeetm0tfN5trD6zgv5gtm/N4baE1Gdj4fxvzDnuuegvmPHLOutA8fK/2KOzMjnd6uJHqTVCQ9att8noieyhFTeFW7//+6cvS8Z2l7znUB8+lzIDOH/Rk5dBbnKoSHDzxNjahc9iyqBVu/KvLZh4O80JxNA17kpZ+tC5xdaVl08Cv5luXqt3+mfo1opo46O+B8N7yzBBH4+v6BRdKPblvGze7tZORcTN24kiXEcPlw6Q7SjubywIWlf4Lpx/KIi3YRGxXehwDd7J7Lg1Ff8tuRRdCy5IUPWL+JM16Yy8VdTuHF63uEdfuBVOs2g637MzHG4PEaJi7azt4j2QHn77HicYbnfEYNrAZXb+Y+di+ZxrEfX4EFL9HUY10BG4crtqnLdzJ3494S6eHy34XbePuHJH7+bS+XPT3RsXH2zflJtP/r7FJ7DV2U+wMArrRt4PWSnZvPlOh/MD76ddanpLM2JZ1nv7JKEbn5Xkx+0c+rFoX/m9zCK+Oa2SXr+D+MfoHR7lkcOZbPIzn/YWLMOI7tS8bk5/LH17/i0Sk/ceWbi7jizUVkZZT8PL0bZ5G5/MOAn0nGx7fRM3EsnTOWWHk6YvX6Sc9wbrhOO3SYke5vqW0yS13nsSMHHNNrHVgDUDAcB0CT3BTy5r7AnI17A47JdDgrlyvfXMQr/3iCXf++ptT5aojVRuVOTy51nlDUkFyarhxHbr6H+mQE3aEgffc2dm3/1XqflVekXvy/C7fxv/W7Mcaw4fcjrE89UmL5T2Oe4/no9zmUVvT7PpyVyyvfbyY9q2iQ9noNa3Yddvxs96Rnl/p7e2rGel6d8xv5Hi9er2FdSnqRdXi9hv7/nMejn60NuL+ZOfkcLNaJpNR5M9JJWruEv0R9QhNJJ3dfUqnzph/LIy5rD1+t3FKudYdLtSwZ5Hu8/LxlH+9OmcQ119xIq8b1ePbrjWzek0GbRjXJyfPw8MWdSizX2rW/yP+nbZ1M87UbyDXuIg/+dGVbwzWv2XWYjs3qEBvl4qUbd/8jAAAgAElEQVQvfyabGH79Z/muEoM1dfZcWskBWrfNZn7sv/j6l4Z0b3IxxNYhO8+DS4SD899kSvRqkvaeQ7dWDeDwTrweT8EVQSz2jz/rAJnjL+BIbHMGua06zkl7M2jAEaLcdQEYOWkFt2RnFlRlAMRKYQOtySs8KdbJKWwz8Bng3sAA9wa2HX2Fvh7r0dmnr3wKM28Fs2Lrk7/fzZXmBQTDzpRUehZbvuOiPwPg7TMMV5TzYdwyfycI1M63BnSLy9wJs+4nv8mFBfP8svMQY2dtYOSAeJqmfs8D0R/w1V6AgeTmexGBaL/qjGNHnAeHq3N0h2N6bn4+r37wBfVH3cS+jFzenL+FL+45B4CLXv2JJy7rTJPasaxLTeeruPcc11GcN815W+GQTxR/ZSK3xs1hzqFlpB2tw+TFydxzXvtSq4wOH04jKx/+F/s4dSWLjb9fx2VvLOTpK7owsn9bvAae/8YKEolPD+avUR+xxzQk33OpVVWUnY53z0ZixTrZZx5MhRaF1awfL9/JtPkraVAzhpED4gvSpyzexuffzOaJkcPo36FJkTw9981Gvlm7m8WPXwBYvdha7J6HqdmIphwiDzfJB4+ydFsaT81Yz7sjEhjUsTEvfzybM3t24zMzhv+tPwOYWLDOT1fuol3jWiS0bQjAdf9ezIHMHFb9bXCRbX+xKoWPlu3g47vOLvjMfp14D2cc+qbgPOE9tJNb3viG0+Nbc2+/pmQey6ZVq1MxBvanZ7Ek7n6WeLrAisPQ4WKo3zrYrzJolRYMRGQI8C+s08l/jTH/PBHbnTR1KjFJ39KkXU8+jPkHX67JZ2OnG3klejyJKZeR/statpoWJPd5hLaNa5GT7yH10DHaNaldYl3Nj1oHePG7PeOO7mZz0lau/u8m7hgQz92D4vk69kl2m0Z414HLHcUXybHsWL+EQRcPZcOir7j2jseoUyPGMc8HMnN49quNPHJxR05tVAuAjOw86thF6fSsPCbHvEgrOcDKwwMAaJY0De+qO9l+0X/4auFKGtSK5ZnoyQDMT97A3tjTafzOGYi7ZHG83tHt1D72C7X5pSDNveFzfol7nr/lPAMMZFHSAUZHHyuxrE9MbuHVd8P80ktEh7JyaWyHo/h0qw6+haSBwHsx42gvvzMp6ZlSl9+7fR3N47uwL8vQpE4sIsL82Z+Qt/ID+tnj89Syq696pn0Le3bQoub3BctnTbyam/Ib8o/Z9/N4DWtIhgbZu9j37nXcs30AWc0SmP3AAESEz5ZtY9bMH/nA4Wtqmuf8kJiurh18G/sES9e4mPfrMUZlzeWTd/uRk5vHqKO/8q9pF3F2QgJ3uL8pdR+La+Y9fiXMaO8xbo2aA8Cx337k3i++Yqm3C+2b1i5oR3rx63Wc0qA2R/M8jOwfz/43LuCIJ4a+dqlo47LvWRt7P69+ez058z4l9ZJ3qcsxsoklae8RRkVZ+7ov40Wa1vDy2ydP0XH7lII8eDZ9y/yln+DpeQsXbXqKDvm9WBH3GlO2/BMG3MO0ZTuYvWwdV7iX8nXsm8xc3wg6/IGJi7aTne/h3vNOY9Fv+6lPBmtTDjP3q6mkZRxlYsw4AGbENiTD1CRp33lsS9nNJa7lbN7dgWauwzy17WZWJHWis2snnV078Xjfw+0ScvO9zP5yMgeiW/L1ny8kv2YzGu1fQjxZpGedx7sLt7Fwy36+uOcc/jPrB7rlrWf9rg60r5VNg2Zt6H5oTpHP+djBXUw4OIYF+3uwf/VBanvTeaj567jF8Id+7QC7Le+bhznS7Czq3vM9x1ulBAMRcQNvA4OBFGCFiMwyxmw8HttbuH47aT+/z5mDb6LPry/Sw7WdDclWMa1x5iZ2blnCSPdCLju8jBrR1tXxD6t60NadyNMHhrBizTpevfcGehVbb2nDBA/a/R58+B53uf/AshVncF3HKLrIQVrKQfjCagzs5G3Lda5ktn05jVtde/jl/a2ceng5e6+fyYeLNnPP0AtoVb8GR5KWsGRnLmM3/ZFZx57itj/ezCdr0vjPjDm8cve1dE58nrSGZxIvVvVF0yyraNn38BxcYvh9/n94yLsS/Eqzp8+5hVpzsnBLNnhKVo21y95QIq3rvq8BuP7ox3g+XMwlri60dZV+UqqbV1id0tizv0jJyd/BjGN0NF7H6Qmu3wBonzqz1OWbf3QeK9y9+Dz7DM6/8maGdKhD6+XP04GdJZZpkWtdUcdlFVZb9WcN/aPgvdybqIXV1tM7eym1UrN5JiqJK3Z3ZMfBLNamplP/6zv4IGa1Yz4amdKHkwaosWc5Y3J+orn7ABywu9RGwZmuTfxv9Rk8Ev15wOVPFLen8EC5avNjXBUDI3IfY+miNPrPeJqj5z/HbSueZIO3Ld1d21iV8Qz9vduLfNYtfvuAupLF2OgpYODYgjf5MXYjid7TWL2+Ab6+dvmzHiAr+Vs6eo8WyUOPDS8B8MmOTUjUjwzGqr6M3/sdxns3G2a9ypToSfyKddJsumM2v01aROJvDcglCk/CA/zB9T0PxU7ii83vMi7nGfAL4NbFRhoLdu/hrJT3uSRmGu/tbMGRHKvUe4Zf29Ty98dQ45SO1Ol9DZNiXgbA+7qLfZ1u5uMYK4Ct+n00b/2QBBjWpKTztHc8A2I2sOyTtTTIWcymG37itGJjRdXbt4Iakssl7pVWm7LAq7tvJYs4ftkzvsi80XvXlPv7C4VUxvjyItIPGGuMucT+/wkAY8w/SlsmISHBrFy5MrgNefJY9u0U3IkfkpC/mp00pw1F7xbdxKk0N/tL7f2x39SliRxhTs1LGZz1bXDbt+2KaUfr3PKNG59p4qgt2bwcdTfdm7gYsns8+cZFlHjx4MKNlxTTmFZygAVx5zEo+8eA68sxUUWqbyrKl4fyWkwvzsGq/jlg6tJYStYRA8xuOopL9r6Lu5RG02B5cRV0dQ1GvnGRRCtOl6IDtq3xtmNtx/tou3kiA93rK5yvRe6zGOBZVvaMJ6kjpiZ1JQuvEVx+35XvePV3zMQUtG8UNyXmRkbkflqhPBygHrXcXmp4Msqe2bbEnUA/j/N54616j3Jexld08252nO5vhbcjZ9gXJ8Wtju5D09yd1CGLSae+yIM77y8yfWr++QyP+qFIWrqpRT0pGgR9fq55If2z5hX8n0M0sWOd26rKIiKrjDHl6uteWcHgemCIMeZO+/9bgLOMMfcVm28UMAqgTZs2fXfsCLKu1Osl45kW1JHSqzPU8bHJ27rgPoNQ5EosMaZ8jXQVlRnThNq5+8ueUYUkHzdRVXRo7eO5b9kmmtix+yvU3TaYYHBS9yYyxkwwxiQYYxKaNGlS9gLFuVzENjvNWte5jxUke2s1LTKbt04LzMAxhdutYTUQeeq3BSB/5Nzgt22vJ3Pg34JezvvHL8m44J9kX/1fPBdbhSUTbXVZ9HS4BLpdX5DuOf3Kwu2VVpcSJsZV/lrFjjWskoCJqx/SNqM7Xlj2TLb8+vEYd1zQ26h966eYKIflWp9F9oUvkNHzDg5d9ykM+HPQ645UppbVgOsZ8Aje063HYprWzl1GnY47I0Ubm010TTwXji05X5RzX/vStuWNronpfCVe+7dZkF6rKZ5TinYzMJ0uc1wHgIkt7DJr4py7eBZ4IDHwdIBbZnDszoXkNu2Jp05LTF3rudKe5n0C5KFuibSsfo+USDt27+oTct9FZTUgpwL+zeOt7LSwi+l/Pyx6FTnjTnBHQ9p2XPk5sP5z6HQ5bP4G1+BnoHYzWGjVCco14yF5Ie7zn4QjvxPV+DSIrQc5QYyl0/sWZNAYatdvA1tmQKPTYMN0a1r7C2D/b9CiF2z6GgaNgVWT4ZYv4chuXB0upE4H+ySYexSyDyF9b4WUlbhPuwhia1udeHpcjzuuPrx4KtRpjuTnwJEUiKsP2YfhtIsgaS6cfS8s/XfR/J3/JNRsBN88XOwDqwO5zsVw6XY9rJ0G5z4Ge9bB5tml7r7L/qykYTv43bmenW7XwfrAo2VK+wsCbgeAoROg9ZlENYyHfZtg248w9++Q79BVuHFHOFCsuF/nFKRlX9jxM8QPgu0LrO/rju+JAwrCRJuusOi10vMRXRPyHKob218Iw6fCkVR4sy8Yv2qsU/tDv/tg5p8gzDeRVZi4kbg6cHQv7vqtrON0y3fIpf+ElROh1Rnw+y/Q4ybY9DXS6XJ4f0jRVXS/HtZ+AvVaQ/oupN15xPa6Eeb9rSANQG76AD67DXKLdueVM++yPo+4+pCyHLpeC/k5uAY9Ai37IlOuhsPJ0O482PYjrvMegzPuhCO/W5/v1h+QrkPhqwes39vMPxVd/0N2ld/RA1YoW/cZtOgNR/fBrKLVPDSMh8vGQVQsfP0w9L4ZDiVbx0rOEUj+maj4/kS53HDPT+D1wOe3w5EU3EP+Dxq2g8QPYd6zRfPQ5Sr4pWj36JrnPgRLXimS1qBZm3J8aaGrrGCwAuggIvFYQWAY8IfjsqWeN1kvsE66YP3Yd6+BIf+Ay16Cui3BN2RC75uh0xDrBdDYKlnwx89g52KYO7bo+ms2hiyH+rxu10GDU633d/0ILjfUPxViakO/e8GTa43lM+DP0CrBOjmLwCndi64nphZc8KT1vl6rotNq2yWcUT9BTE34eJgVDNqfbwWeNv3g8let/UtZaf2ozn0MEqdaJ6DoGiWDQasE2PaDdZLa8XPRaZe+aH2W8eda6y/rJA3WD6G0YHD9ROgzAtZ+Zm3r0HZwx0Bs3cLPtNUZpa87/lzrpO37fgGanm69Ej+0Apa4wfgV39v0KxkMajaGi8bC8netk8v2BdZnU1yd5mXsa3vYu65kenQN60TSsB384VPYnQinDbbWV8fuQnloDHz3ROD1nygdLoa99smyYTvrM+m4y9qHq9600vuMsP62KXYFf+1/reNn4COwZz1c/oq1v12vtfZ1+DQrIP/wf/a2BsNfU2FssavzU3rAfStg208w5SroORw6Xlw4vd991on/6n9bv+XTLrLS67aw83eL9fd6u2to3jGo0QC+uMP6v0b9on/Ptz/7Q35V0b3+CE27WO/PvMv6e/oVVoBy+VWqGEPBEL0i4I6CS16AtgOh9VnWvAMfgfnPF70QaNEbouLsVyxkpUFcXbhzHnjzYWLhXfonQqUEA2NMvojcB3yH1bV0ojGmZBeW4yV+ENxfrFGpTjO4dyk0iHdeps1Z1qt4MOh7K6z/0joI0vwaiRu1L3zvtj/mwf5dJK0uotS0qqQIpRjYpKP194KnYNl4uOI16yA+487Cg334VMjYA6d0swKCq1if8Zja1tVZ5yutYHL6FfCmXcQd/glk/G6tq73Vb5vOV8Lg52Dzt1aQ9BdXD7LTrYPcFxBL0+486/WaHQRHzISWCfC8XS3YuGPpy175L+uqzcnFz1tXezuWQNpWqNPC2ofG9l2n7ljw9ZyJioHWZ1qv3faNRr799Ff8MyuuUTvnYHCK312kHQZbr+LOvAtOvxz+Vc47Tn2fcbgN+xhOPQeW/BsWvFR4MowqY+iPFr0hqgb0uMF6AdxrHxen9iucr9Ol1t/ri91TUfyiyve9tjsXHk2C2sWqif0/x3otKZPvZJ62vTBgOKnXyrrI6DkMejlcn/p+r/6cfrv128BZo4qm+QLBZeNg3efQ+WrrN1pcqwSrdFGjISTcXnpew6zS7jMwxswGynFpeQI1LTn+TpnaDoALn4ZPbraCwYVPW1cg9U9M0a4I/xLNuX8pOq1WY+sFzie1Gg2sYFCzIZxhXz1d/opVbdRpSMn5o2Kh/wOwy6GHjO9EVeeUsq+mfbpeA4vfsK5Eo2Lg1q+tq6SYmtZVYNuBMPWmossEWne784oGmUuehy1zrKu9ui2gbiuYeHHJ5Zr3gJHfW3+dPLTOqiL5dETJaU06AzOLpl39b2vfyuKOLjtwglX6SNtq/S2txBWK0y+3/g561Lr6r9008Pw+d8yxrmYr6pYvre+n61CrCsn//pfigSAU544JPN3lhltnhW97TtqdVxicAuVjTBLIiWvWrZZ3IIfFOffD4jcLr5x8vbIadYAuV1VevoI17GPY9yustm/68W/UcrpqKc7tcAeWr0GuYTto0NZ5OV+x3uf8v0LvW6wAAhDvN27NJS8UnXfws5D8M0SXo7G413D46UWruqmbffe37+/g55yvEtucVfr66reBo6X0PCp+MeGKgt5/LDuP/lqfBQeTIOug8/Qr/wXbf7I+9+MRDHyiYoO769UdXfQEHqzmPa0XFC1VVyVXv221OZX2myiurJJomJ3UvYlOSr6eSP3uh6f2FZ68etxo/Q1UrXEyOv1y6yrQa9erl9WzojinXji17c/k1HOcq90e22FVPfmLrlFY3VWW/g/CH8vZV33go3DPEueSWv8HoPv15VuPvxi7J0qNBlabzF3zrbafFr2LzufXg63c/vg53Otf2rKrIAbZJb2Wfa3qwBoNgl93WeTEnnyqnd43w/2rQguax5GWDIJ1y3TY8l1hw59Pl6vhbwdO2i+6TF2vgSVvWVfzwYiySwauaLCfhUzC7VZPkN52NUP9NlYbhK9HU1y9irWRPLQe3xDAQeWvWZfgtxVIw3ZWT7SzR1vtT2CdpHPtm4hOG2zViceUHMKkTHF2yez69yFjt9XLbet8q+Q06NHCuvvo4zDmv1MpT1UbGgyCdUo36+UkUgMBWA2u59zv3EAWiNs+OcXUsrqzgtUA56t7BqueHQqDQUUby0/AYF3l4o6C4R+XTI+pBSO/s6oBgi1hFdft2sL3vtKLfyNuTK3Sl3VFBV9/3+dWqz1FVVsaDJRFpLDKKxhRDsGgOl9hFu9qebwEKnVUJBhc9UZo+VERT4OBCo1TtUVpJaSLn4djgQd0U+XkdB+EjysaCPxsDqWK02CgQuOrJvLv2VNaNdA59zunq+AF6nJ4gnuhqKpBexOp0PhKAdW5aqgyFHRgcAi8kdx2pSqNBgMVHhoMTqyG7eCR3+Cc+0pOc2kwUMHTYKBCZHf1DGJEUxUmdZo53xug34WqAA0GKjS+O6+1ZFA5nNoO3BoMVPD0qFEhsoNBWQOZqePDKRgEUzIY/knJGyhVtaTBQIXGa4/EqCWDyuEYDMrRZnD6FdbzLJwGIVTVklYTqdB0v956pkB5BrVT4VfRaqJhH+mNZqoILRmo0DSMtwbfytRnCFcKp3sKtAFZVYCWDFR4hDoWj6oYpxv8tGupqgC9hFDhERVjPWu5o9ZBn1ChNiArZdOjRoXPkH9Udg6qH8dgoMNRqOCFVE0kIi+LyCYRWSsi00Wkvp3eVkSOiUii/XrHb5m+IrJORJJE5A2RUB7+q1Q1pyUDFSahthnMAboZY3oAvwFP+E3baozpZb9G+6WPB+4COtgvrVdQqqKc7kDWsYlUBYQUDIwx3xtjfAOnLwVaBZpfRJoDdY0xS40xBpgClONp4UopR1oyUGESzqNmJOD/YNt4EfkFOAI8ZYxZCLQEUvzmSbHTlFIVETAYCCUeE3rpy8c7RypClRkMRGQu4PQIrCeNMTPteZ4E8oGP7Gm7gTbGmIMi0heYISJdg82ciIwCRgG0aePwQHOlqjvHm86iC/96cotOO2vU8c+TikhlBgNjzEWBpovIbcAVwIV21Q/GmBwgx36/SkS2Ah2BVIpWJbWy00rb9gRgAkBCQkKQT0JXqhpwBSgZuKJKBgOlShFqb6IhwF+Aq4wxWX7pTUSsli0RaYfVULzNGLMbOCIiZ9u9iEYAM0PJg1LVWqBqIm07UEEI9Wh5C4gF5tg9RJfaPYcGAc+KSB7gBUYbY9LsZe4FJgE1gG/tl1KqIgIGA73fQJVfSMHAGHNaKelfAF+UMm0l0C2U7SqlbIHaDLRkoIKgYxMpFckCDWGtwUAFQYOBUpHM8bGX7tKnKVUKDQZKRbKA1UQaDFT5aTBQKpI5BQNfiUCDgQqCBgOlIpnTOI++AKHVRCoIGgyUimSBrv61AVkFQYOBUpHMsZrILi1oNZEKggYDpSKZUzAozzSlitGjRalI5nTCN6b0aUqVQo8WpSKZ4wnfDgbaZqCCoMFAqUjm1GPIVzLQNgMVBA0GSkUyx2oib+nTlCqFHi1KRTKn+wx81UR6n4EKggYDpSJZoAZkpwffKFUKPVqUimSBGpC1ZKCCoMFAqUjm1EisDciqAjQYKBXJnEoGdZpbfxt3OrF5URFNOyIrFcmcgsEZd0CTThAVB0vfPvF5UhFJSwZKRbLSnmfQ/nztWqqCEtLRIiJjRSRVRBLt12V+054QkSQR2Swil/ilD7HTkkTk8VC2r1S1F6iR2BcM9E5kVQ7hOEpeM8aM808QkS7AMKAr0AKYKyId7clvA4OBFGCFiMwyxmwMQz6Uqn4c7zMoNi2qBuRmnJj8qIh1vC4ZrgamGWNygO0ikgScaU9LMsZsAxCRafa8GgyUqohAPYYKhrLW6iJVtnAcJfeJyFoRmSgiDey0lsAuv3lS7LTS0pVSFREVV/q04m0Gzbod37yoiFZmyUBE5gKnOEx6EhgPPId1l8tzwCvAyHBlTkRGAaMA2rRpE67VKlV1BGwP8KtCeuAXqNn4uGdHRa4yg4Ex5qLyrEhE3gW+tv9NBVr7TW5lpxEg3WnbE4AJAAkJCaY8+VCqWvHdYIZQcOexj3/JoGG7E5UjFaFC7U3U3O/focB6+/0sYJiIxIpIPNABWA6sADqISLyIxGA1Ms8KJQ9KVWt17EL7wEdKTtOupSoIoTYgvyQivbAuSZKBuwGMMRtE5FOshuF84E/GGA+AiNwHfAe4gYnGmA0h5kGp6iu2NoxNh0PJsHBc0Wm+YKBBQZVDSMHAGHNLgGkvAC84pM8GZoeyXaVUcQ5dTH29iTQYqHLQo0SpqsDphF9QMtAB61TZNBgoVRU43Xym1UQqCHqUKFUlON2JrNVEqvz0KFGqKnCsJvLdgazVRKpsGgyUqgqcqomia1h/87NPbF5URNJgoFSV4BAMajeDmNpw4d9PfHZUxNGxbZWqCpyqiVxu+GupN/grVYSWDJSqCgINZa1UOWgwUKpK0GCgQqPBQKmqQEsGKkQaDJSqCjQYqBBpMFCqStBgoEKjwUCpqkDvMlYh0iNIqapAq4lUiDQYKFUlaDBQodFgoFRVoNVEKkR6BClVFWg1kQqRBgOlqgQNBio0GgyUqgq0mkiFKKQjSEQ+EZFE+5UsIol2elsROeY37R2/ZfqKyDoRSRKRN0S0fKtUyPRnpEIU0qilxpibfO9F5BUg3W/yVmNML4fFxgN3AcuA2cAQ4NtQ8qGU0mCgQhOWsqV9dX8jMLWM+ZoDdY0xS40xBpgCXBOOPChVrWnJQIUoXBWNA4G9xpgtfmnxIvKLiPwkIgPttJZAit88KXaaIxEZJSIrRWTl/v37w5RVpaogbTNQISqzmkhE5gKnOEx60hgz034/nKKlgt1AG2PMQRHpC8wQka7BZs4YMwGYAJCQkGCCXV6pakNLBipEZQYDY8xFgaaLSBRwLdDXb5kcIMd+v0pEtgIdgVSgld/irew0pZRSlSgcj728CNhkjCmo/hGRJkCaMcYjIu2ADsA2Y0yaiBwRkbOxGpBHAG+GIQ9KqavegkanVXYuVIQKRzAYRsmG40HAsyKSB3iB0caYNHvavcAkoAZWLyLtSaRUOPS5pbJzoCJYyMHAGHObQ9oXwBelzL8S6BbqdpVSSoWPdkFQSimlwUAppZQGA6WUUmgwUEophQYDpZRSaDBQSimFBgOllFKAWIOHnvxEZD+wo4KLNwYOhDE7kUD3ueqrbvsLus/BOtUY06Q8M0ZMMAiFiKw0xiRUdj5OJN3nqq+67S/oPh9PWk2klFJKg4FSSqnqEwwmVHYGKoHuc9VX3fYXdJ+Pm2rRZqCUUiqw6lIyUEopFUCVDgYiMkRENotIkog8Xtn5CScRSRaRdSKSKCIr7bSGIjJHRLbYfxvY6SIib9ifw1oR6VO5uS8fEZkoIvtEZL1fWtD7KCK32vNvEZFbK2NfyquUfR4rIqn2d50oIpf5TXvC3ufNInKJX3rEHPsi0lpEfhCRjSKyQUQetNOr5HcdYH8r93s2xlTJF+AGtgLtgBhgDdClsvMVxv1LBhoXS3sJeNx+/zjwov3+MqyHCAlwNrCssvNfzn0cBPQB1ld0H4GGwDb7bwP7fYPK3rcg93ks8KjDvF3s4zoWiLePd3ekHftAc6CP/b4O8Ju9b1Xyuw6wv5X6PVflksGZQJIxZpsxJheYBlxdyXk63q4GJtvvJwPX+KVPMZalQH0RaV4ZGQyGMWYBkFYsOdh9vASYY4xJM8YcAuYAQ45/7iumlH0uzdXANGNMjjFmO5CEddxH1LFvjNltjFltv88AfgVaUkW/6wD7W5oT8j1X5WDQEtjl938KgT/wSGOA70VklYiMstOaGWN22+/3AM3s91Xpswh2H6vKvt9nV4lM9FWXUAX3WUTaAr2xnpFe5b/rYvsLlfg9V+VgUNUNMMb0AS4F/iQig/wnGqt8WaW7ilWHfbSNB9oDvYDdwCuVm53jQ0RqYz0u9yFjzBH/aVXxu3bY30r9nqtyMEgFWvv938pOqxKMMan2333AdKwi415f9Y/9d589e1X6LILdx4jfd2PMXmOMxxjjBd7F+q6hCu2ziERjnRg/MsZ8aSdX2e/aaX8r+3uuysFgBdBBROJFJAYYBsyq5DyFhYjUEpE6vvfAxcB6rP3z9aC4FZhpv58FjLB7YZwNpPsVvyNNsPv4HXCxiDSwi90X22kRo1j7zlCs7xqsfR4mIrEiEg90AJYTYce+iAjwHvCrMeZVv0lV8rsubX8r/Xuu7Jb14/nC6nXwG1aL+5OVnZ8w7lc7rJ4Da4ANvn0DGgHzgC3AXKChnS7A2/bnsA5IqOx9KOd+TsUqLudh1YfeUZF9BEZiNbolAbdX9n5VYJ8/sPdprf1jb+43/5P2Pm8GLnVfnRYAAABUSURBVPVLj5hjHxiAVQW0Fki0X5dV1e86wP5W6vesdyArpZSq0tVESimlykmDgVJKKQ0GSimlNBgopZRCg4FSSik0GCillEKDgVJKKTQYKKWUAv4fNzGa9JfrpGEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f56207ac3d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plt.plot(new_data[0,:,0],label='After conv')\n",
    "plt.plot(new_data[0,:,0],label='After conv with zero pad inside')\n",
    "plt.plot(filtered,label='After FIR')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAY8AAAEACAYAAABLfPrqAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f921a68a750>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plt.plot(new_data[0,:,0],label='After conv')\n",
    "plt.plot(np.pad(new_data_[0,:,0],(30,30),'constant',constant_values=(30, 30)),label='After conv with zero pad')\n",
    "plt.plot(filtered,label='After FIR1 (zero phase)')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Implementing gammatone inside conv layer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras import backend as K\n",
    "from keras.engine.topology import Layer\n",
    "from keras.engine.topology import InputSpec\n",
    "import tensorflow as tf\n",
    "import numpy as np\n",
    "from keras.utils import conv_utils\n",
    "from keras.layers import activations, initializers, regularizers, constraints"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZ8AAAEACAYAAABs0nsCAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nagZcAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f91ac15f510>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# t = np.arange(80)/float(1000)\n",
    "t = tf.range(start=0,limit=80./1000.,delta=1/1000.,dtype=K.floatx())\n",
    "t = tf.expand_dims(input=t,axis=-1)\n",
    "a = tf.constant([100,100,100],shape=(3,1),dtype=K.floatx())\n",
    "beta = tf.constant([30,30,30],shape=(3,1),dtype=K.floatx())\n",
    "fc = tf.constant([10,20,30],shape=(3,1),dtype=K.floatx())\n",
    "n = tf.constant(4,shape=(1,1),dtype=K.floatx())\n",
    "res = tf.multiply(tf.multiply(\n",
    "    tf.matmul(a,tf.pow(x=t,y=n-1),transpose_b=True),\n",
    "    tf.exp(tf.multiply(-2*np.pi,tf.matmul(beta,t,transpose_b=True)))),\n",
    "    tf.cos(tf.multiply(2*np.pi,tf.matmul(fc,t,transpose_b=True))))\n",
    "with tf.Session() as sess:\n",
    "    plt.plot(sess.run(res)[1])\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor 'range_43:0' shape=(80,) dtype=float32>"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# conv_utils.normalize_tuple(80, 1, 'kernel_size') + (1,2)\n",
    "# for input_channel in range(1,(80,2,3)[1]):\n",
    "#     print(input_channel)\n",
    "t = tf.range(start=0,limit=80/float(1000),\n",
    "                          delta=1/float(1000),dtype=K.floatx())\n",
    "t"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Conv1D_gammatone(Layer):\n",
    "\n",
    "    def __init__(self, filters = 1, kernel_size = 80, rank=1, strides=1, padding='valid',\n",
    "                 data_format='channels_last',dilation_rate=1, activation=None, use_bias=True,\n",
    "                 fsHz = 1000.,\n",
    "                 fc_initializer = initializers.RandomUniform(minval=10,maxval=400),\n",
    "                 n_order_initializer = initializers.constant(4.),\n",
    "                 amp_initializer = initializers.constant(10**5),\n",
    "                 beta_initializer = initializers.RandomNormal(mean=30,stddev=6),\n",
    "                 bias_initializer='zeros',\n",
    "                 **kwargs):\n",
    "        super(Conv1D_gammatone, self).__init__(**kwargs)\n",
    "        self.rank = rank\n",
    "        self.filters = filters\n",
    "        self.kernel_size_=kernel_size\n",
    "        self.kernel_size = conv_utils.normalize_tuple(kernel_size, rank, 'kernel_size')\n",
    "        self.strides = conv_utils.normalize_tuple(strides, rank, 'strides')\n",
    "        self.padding = conv_utils.normalize_padding(padding)\n",
    "        self.data_format = conv_utils.normalize_data_format(data_format)\n",
    "        self.dilation_rate = conv_utils.normalize_tuple(dilation_rate, rank, 'dilation_rate')\n",
    "        self.activation = activations.get(activation)\n",
    "        self.use_bias = use_bias\n",
    "        self.bias_initializer = initializers.get(bias_initializer)\n",
    "        self.fc_initializer = initializers.get(fc_initializer)\n",
    "        self.n_order_initializer = initializers.get(n_order_initializer)\n",
    "        self.amp_initializer = initializers.get(amp_initializer)\n",
    "        self.beta_initializer = initializers.get(beta_initializer)\n",
    "        self.input_spec = InputSpec(ndim=self.rank + 2)\n",
    "        \n",
    "        self.fsHz = fsHz\n",
    "        self.t = tf.range(start=0,limit=kernel_size/float(fsHz),\n",
    "                          delta=1/float(fsHz),dtype=K.floatx())\n",
    "        self.t = tf.expand_dims(input=self.t,axis=-1)\n",
    "\n",
    "    \n",
    "    def build(self, input_shape):\n",
    "        if self.data_format == 'channels_first':\n",
    "            channel_axis = 1\n",
    "        else:\n",
    "            channel_axis = -1\n",
    "        if input_shape[channel_axis] is None:\n",
    "            raise ValueError('The channel dimension of the inputs '\n",
    "                             'should be defined. Found `None`.')\n",
    "        input_dim = input_shape[channel_axis]\n",
    "        self.kernel_shape = self.kernel_size + (input_dim, self.filters)\n",
    "\n",
    "        ## Add learnable parameters\n",
    "        self.fc = self.add_weight(shape=(self.filters,1),\n",
    "                                      initializer=self.fc_initializer,\n",
    "                                      name='fc')\n",
    "        self.n_order = self.add_weight(shape=(1,1),\n",
    "                                      initializer=self.n_order_initializer,\n",
    "                                      name='n')\n",
    "        self.amp = self.add_weight(shape=(self.filters,1),\n",
    "                                      initializer=self.amp_initializer,\n",
    "                                      name='a')\n",
    "        self.beta = self.add_weight(shape=(self.filters,1),\n",
    "                                      initializer=self.beta_initializer,\n",
    "                                      name='beta')\n",
    "        if self.use_bias:\n",
    "            self.bias = self.add_weight(shape=(self.filters,),\n",
    "                                        initializer=self.bias_initializer,\n",
    "                                        name='bias',\n",
    "                                        regularizer=self.bias_regularizer,\n",
    "                                        constraint=self.bias_constraint)\n",
    "        else:\n",
    "            self.bias = None\n",
    "        \n",
    "        # Set input spec.\n",
    "        self.input_spec = InputSpec(ndim=self.rank + 2,\n",
    "                                    axes={channel_axis: input_dim})\n",
    "        self.built = True\n",
    "    \n",
    "\n",
    "    def call(self, inputs):\n",
    "        \n",
    "        # Get gammatone impulse response\n",
    "        \n",
    "        gammatone = self.impulse_gammatone()\n",
    "        gammatone = tf.expand_dims(gammatone,axis=-2) ## Considering single input channel\n",
    "        \n",
    "        if self.kernel_shape[1] > 1:\n",
    "            raise ValueError('Number of channels for input to gammatone layer'\n",
    "                             'should be 1.')\n",
    "        \n",
    "        outputs = K.conv1d(\n",
    "            inputs,\n",
    "            gammatone,\n",
    "            strides=self.strides[0],\n",
    "            padding='same',\n",
    "            data_format=self.data_format,\n",
    "            dilation_rate=self.dilation_rate[0])\n",
    "    \n",
    "        if self.use_bias:\n",
    "            outputs = K.bias_add(\n",
    "                outputs,\n",
    "                self.bias,\n",
    "                data_format=self.data_format)\n",
    "\n",
    "        if self.activation is not None:\n",
    "            return self.activation(outputs)\n",
    "        return outputs\n",
    "\n",
    "    def compute_output_shape(self, input_shape):\n",
    "        if self.data_format == 'channels_last':\n",
    "            space = input_shape[1:-1]\n",
    "            new_space = []\n",
    "            for i in range(len(space)):\n",
    "                new_dim = conv_utils.conv_output_length(\n",
    "                    space[i],\n",
    "                    self.kernel_size[i],\n",
    "                    padding=self.padding,\n",
    "                    stride=self.strides[i],\n",
    "                    dilation=self.dilation_rate[i])\n",
    "                new_space.append(new_dim)\n",
    "            return (input_shape[0],) + tuple(new_space) + (self.filters,)\n",
    "        if self.data_format == 'channels_first':\n",
    "            space = input_shape[2:]\n",
    "            new_space = []\n",
    "            for i in range(len(space)):\n",
    "                new_dim = conv_utils.conv_output_length(\n",
    "                    space[i],\n",
    "                    self.kernel_size[i],\n",
    "                    padding=self.padding,\n",
    "                    stride=self.strides[i],\n",
    "                    dilation=self.dilation_rate[i])\n",
    "                new_space.append(new_dim)\n",
    "            return (input_shape[0], self.filters) + tuple(new_space)\n",
    "    \n",
    "    def impulse_gammatone(self):\n",
    "        \n",
    "        gammatone = tf.multiply(tf.multiply(\n",
    "                        tf.matmul(self.amp,tf.pow(x=self.t,y=self.n_order-1),\n",
    "                                  transpose_b=True),\n",
    "                        tf.exp(tf.multiply(-2*np.pi,tf.matmul(self.beta,self.t,\n",
    "                                                              transpose_b=True)))),\n",
    "                        tf.cos(tf.multiply(2*np.pi,tf.matmul(self.fc,self.t,\n",
    "                                                             transpose_b=True))))\n",
    "        gammatone = tf.transpose(gammatone)\n",
    "        return gammatone\n",
    "    \n",
    "\n",
    "    def get_config(self):\n",
    "        config = {\n",
    "            'rank': self.rank,\n",
    "            'filters': self.filters,\n",
    "            'kernel_size': self.kernel_size_,\n",
    "            'strides': self.strides,\n",
    "            'padding': self.padding,\n",
    "            'data_format': self.data_format,\n",
    "            'dilation_rate': self.dilation_rate,\n",
    "            'activation': activations.serialize(self.activation),\n",
    "            'use_bias': self.use_bias,\n",
    "            'fsHz': self.fsHz,\n",
    "            'fc_initializer': initializers.serialize(self.fc_initializer),\n",
    "            'n_order_initializer': initializers.serialize(self.n_order_initializer),\n",
    "            'amp_initializer': initializers.serialize(self.amp_initializer),\n",
    "            'beta_initializer': initializers.serialize(self.beta_initializer),\n",
    "            'bias_initializer': initializers.serialize(self.bias_initializer),\n",
    "        }\n",
    "        base_config = super(Conv1D_gammatone, self).get_config()\n",
    "        return dict(list(base_config.items()) + list(config.items()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def FIRnet_gammatone(input_size):\n",
    "    input1=Input(shape=(input_size,1))\n",
    "    x = Conv1D_gammatone(kernel_size=81,filters=3,fsHz=1000,use_bias=False)(input1)\n",
    "#     x = Conv1D_gammatone(kernel_size=80,filters=3,fsHz=1000,use_bias=False)(x)\n",
    "    model =Model(input1,x)\n",
    "    model.compile(optimizer='rmsprop',\n",
    "                  loss='binary_crossentropy',\n",
    "                  metrics=['accuracy'])    \n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1, 2500, 3)\n"
     ]
    }
   ],
   "source": [
    "model = FIRnet_gammatone(2500)\n",
    "t = np.reshape(data[:2500]/float(np.max(data[:2500])),[1,2500,1])\n",
    "new_data_ = model.predict(t)\n",
    "print new_data_.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAm4AAAJSCAYAAAB6EEnFAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nJDibprxbRAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7efc215cd5d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plt.plot(new_data[0,:,0],label='After conv')\n",
    "plt.figure(figsize=(10,10))\n",
    "plt.plot(10*data[:2500]/float(np.var(data[:2500])),label='Original signal')\n",
    "plt.plot(new_data_[0,:,0],label='After gammatone 1')\n",
    "plt.plot(new_data_[0,:,1],label='After gammatone 2')\n",
    "plt.plot(new_data_[0,:,2],label='After gammatone 3')\n",
    "# plt.plot(filtered,label='After FIR1 (zero phase)')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[array([[ 57.85155],\n",
      "       [113.01084],\n",
      "       [167.99686]], dtype=float32), array([[4.]], dtype=float32), array([[100000.],\n",
      "       [100000.],\n",
      "       [100000.]], dtype=float32), array([[22.691906],\n",
      "       [21.749432],\n",
      "       [36.581142]], dtype=float32)]\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYgAAAEDCAYAAAAvNJM9AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n/wNbikz1fVLnzwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7efc2155cb50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(model.layers[1].get_weights())\n",
    "with K.get_session() as sess:\n",
    "    impulse_gammatone = sess.run(model.layers[1].impulse_gammatone())\n",
    "plt.plot(impulse_gammatone)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "30\n"
     ]
    }
   ],
   "source": [
    "kernel_size=60\n",
    "if kernel_size%2:\n",
    "    kernel_size = conv_utils.normalize_tuple(kernel_size/2+1,1,'kernel_size') \n",
    "else:\n",
    "    kernel_size = conv_utils.normalize_tuple(kernel_size/2,1,'kernel_size')\n",
    "print kernel_size[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Linear Convolutional filter. Learns only half the filter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Conv1D_zerophase_linear(Layer):\n",
    "    \n",
    "    def __init__(self,filters,kernel_size,rank=1,strides=1,padding='valid',data_format='channels_last',dilation_rate=1,activation=None,use_bias=True,kernel_initializer='glorot_uniform',bias_initializer='zeros',kernel_regularizer=None,bias_regularizer=None,activity_regularizer=None,kernel_constraint=None,bias_constraint=None,**kwargs):\n",
    "        super(Conv1D_zerophase_linear, self).__init__(**kwargs)\n",
    "        self.rank = rank\n",
    "        self.filters = filters\n",
    "        if kernel_size%2:\n",
    "            self.kernel_size = conv_utils.normalize_tuple(kernel_size/2+1,rank,'kernel_size') \n",
    "        else:\n",
    "            self.kernel_size = conv_utils.normalize_tuple(kernel_size/2,rank,'kernel_size')\n",
    "        print self.kernel_size[0]\n",
    "        self.strides = conv_utils.normalize_tuple(strides, rank, 'strides')\n",
    "        self.padding = conv_utils.normalize_padding(padding)\n",
    "        self.data_format = conv_utils.normalize_data_format(data_format)\n",
    "        self.dilation_rate = conv_utils.normalize_tuple(dilation_rate, rank, 'dilation_rate')\n",
    "        self.activation = activations.get(activation)\n",
    "        self.use_bias = use_bias\n",
    "        self.kernel_initializer = initializers.get(kernel_initializer)\n",
    "        self.bias_initializer = initializers.get(bias_initializer)\n",
    "        self.kernel_regularizer = regularizers.get(kernel_regularizer)\n",
    "        self.bias_regularizer = regularizers.get(bias_regularizer)\n",
    "        self.activity_regularizer = regularizers.get(activity_regularizer)\n",
    "        self.kernel_constraint = constraints.get(kernel_constraint)\n",
    "        self.bias_constraint = constraints.get(bias_constraint)\n",
    "        self.input_spec = InputSpec(ndim=self.rank + 2)\n",
    "        \n",
    "    def build(self, input_shape):\n",
    "        if self.data_format == 'channels_first':\n",
    "            channel_axis = 1\n",
    "        else:\n",
    "            channel_axis = -1\n",
    "        if input_shape[channel_axis] is None:\n",
    "            raise ValueError('The channel dimension of the inputs '\n",
    "                             'should be defined. Found `None`.')\n",
    "        input_dim = input_shape[channel_axis]\n",
    "        kernel_shape = self.kernel_size + (input_dim, self.filters)\n",
    "\n",
    "        self.kernel = self.add_weight(shape=kernel_shape,\n",
    "                                      initializer=self.kernel_initializer,\n",
    "                                      name='kernel',\n",
    "                                      regularizer=self.kernel_regularizer,\n",
    "                                      constraint=self.kernel_constraint)\n",
    "        if self.use_bias:\n",
    "            self.bias = self.add_weight(shape=(self.filters,),\n",
    "                                        initializer=self.bias_initializer,\n",
    "                                        name='bias',\n",
    "                                        regularizer=self.bias_regularizer,\n",
    "                                        constraint=self.bias_constraint)\n",
    "        else:\n",
    "            self.bias = None\n",
    "        # Set input spec.\n",
    "        self.input_spec = InputSpec(ndim=self.rank + 2,\n",
    "                                    axes={channel_axis: input_dim})\n",
    "        self.built = True\n",
    "\n",
    "\n",
    "    def call(self, inputs):\n",
    "        \n",
    "        if self.kernel_size[0]%2==0:\n",
    "            flipped = tf.reverse(self.kernel,axis=[0])\n",
    "        else:\n",
    "            flipped = tf.reverse(self.kernel[1:,:,:],axis=[0])\n",
    "#         print (flipped)\n",
    "        conv_kernel = tf.concat([flipped,self.kernel],axis=0)\n",
    "#         print (conv_kernel)\n",
    "        \n",
    "        \n",
    "        outputs = K.conv1d(\n",
    "                inputs,\n",
    "                conv_kernel,\n",
    "                strides=self.strides[0],\n",
    "                padding='same',\n",
    "                data_format=self.data_format,\n",
    "                dilation_rate=self.dilation_rate[0])\n",
    "#         print tf.shape(outputs)\n",
    "        outputs = tf.reverse(outputs,axis=[1])\n",
    "        outputs = K.conv1d(\n",
    "                outputs,\n",
    "                conv_kernel,\n",
    "                strides=self.strides[0],\n",
    "                padding=self.padding,\n",
    "                data_format=self.data_format,\n",
    "                dilation_rate=self.dilation_rate[0])\n",
    "#         print tf.shape(outputs)\n",
    "        outputs = tf.reverse(outputs,axis=[1])\n",
    "        if self.use_bias:\n",
    "            outputs = K.bias_add(\n",
    "                outputs,\n",
    "                self.bias,\n",
    "                data_format=self.data_format)\n",
    "\n",
    "        if self.activation is not None:\n",
    "            return self.activation(outputs)\n",
    "        \n",
    "        return outputs\n",
    "    \n",
    "    def compute_output_shape(self, input_shape):\n",
    "        if self.data_format == 'channels_last':\n",
    "            space = input_shape[1:-1]\n",
    "            new_space = []\n",
    "            for i in range(len(space)):\n",
    "                new_dim = conv_utils.conv_output_length(\n",
    "                    space[i],\n",
    "                    self.kernel_size[i],\n",
    "                    padding=self.padding,\n",
    "                    stride=self.strides[i],\n",
    "                    dilation=self.dilation_rate[i])\n",
    "                new_space.append(new_dim)\n",
    "            return (input_shape[0],) + tuple(new_space) + (self.filters,)\n",
    "        if self.data_format == 'channels_first':\n",
    "            space = input_shape[2:]\n",
    "            new_space = []\n",
    "            for i in range(len(space)):\n",
    "                new_dim = conv_utils.conv_output_length(\n",
    "                    space[i],\n",
    "                    self.kernel_size[i],\n",
    "                    padding=self.padding,\n",
    "                    stride=self.strides[i],\n",
    "                    dilation=self.dilation_rate[i])\n",
    "                new_space.append(new_dim)\n",
    "            return (input_shape[0], self.filters) + tuple(new_space)\n",
    "\n",
    "        \n",
    "    def get_config(self):\n",
    "        config = {\n",
    "            'rank': self.rank,\n",
    "            'filters': self.filters,\n",
    "            'kernel_size': self.kernel_size,\n",
    "            'strides': self.strides,\n",
    "            'padding': self.padding,\n",
    "            'data_format': self.data_format,\n",
    "            'dilation_rate': self.dilation_rate,\n",
    "            'activation': activations.serialize(self.activation),\n",
    "            'use_bias': self.use_bias,\n",
    "            'kernel_initializer': initializers.serialize(self.kernel_initializer),\n",
    "            'bias_initializer': initializers.serialize(self.bias_initializer),\n",
    "            'kernel_regularizer': regularizers.serialize(self.kernel_regularizer),\n",
    "            'bias_regularizer': regularizers.serialize(self.bias_regularizer),\n",
    "            'activity_regularizer': regularizers.serialize(self.activity_regularizer),\n",
    "            'kernel_constraint': constraints.serialize(self.kernel_constraint),\n",
    "            'bias_constraint': constraints.serialize(self.bias_constraint)\n",
    "        }\n",
    "        base_config = super(Conv1D_zerophase_linear, self).get_config()\n",
    "        return dict(list(base_config.items()) + list(config.items()))\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "def FIRnet_linearphase(input_size):\n",
    "    input1=Input(shape=(input_size,1))\n",
    "    x = Conv1D_zerophase_linear(1,61,use_bias=False,weights=[b1[30:]])(input1)\n",
    "    model =Model(input1,x)\n",
    "    model.compile(optimizer='rmsprop',\n",
    "                  loss='binary_crossentropy',\n",
    "                  metrics=['accuracy'])    \n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "31\n",
      "[[[ -8.50337887]\n",
      "  [ -3.51722765]\n",
      "  [  2.27529454]\n",
      "  ..., \n",
      "  [ -5.42623615]\n",
      "  [ -8.63265038]\n",
      "  [-12.50613785]]]\n"
     ]
    }
   ],
   "source": [
    "model = FIRnet_linearphase(2500)\n",
    "t = np.reshape(data[:2500],[1,2500,1])\n",
    "new_data_ = model.predict(t)\n",
    "print new_data_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5620380510>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(np.pad(new_data_[0,:,0],(30,30),'constant',constant_values=(30, 30)),label='After conv1_zerophase with zero pad')\n",
    "plt.plot(filtered,label='After FIR1 (zero phase)')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
